import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		1





			@register_to_config
			def __init__(  self ,  lowerCAmelCase__ = 10_00 ,  lowerCAmelCase__ = None		)	->   Dict:

							'''simple docstring'''


							self.set_timesteps(lowerCAmelCase__		)

							# standard deviation of the initial noise distribution
							__lowercase					     =				1.0

							# For now we only support F-PNDM, i.e. the runge-kutta method
							# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
							# mainly at formula (9), (12), (13) and the Algorithm 2.
							__lowercase					     =				4

							# running values
							__lowercase					     =				[]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				num_inference_steps
							__lowercase					     =				torch.linspace(1 ,  0 ,  num_inference_steps + 1		)[:-1]
							__lowercase					     =				torch.cat([steps, torch.tensor([0.0]		)]		)

							if self.config.trained_betas is not None:
											__lowercase					     =				torch.tensor(self.config.trained_betas ,  dtype=torch.floataa		)
							else:
											__lowercase					     =				torch.sin(steps * math.pi / 2		) ** 2

							__lowercase					     =				(1.0 - self.betas**2) ** 0.5

							__lowercase					     =				(torch.atana(self.betas ,  self.alphas		) / math.pi * 2)[:-1]
							__lowercase					     =				timesteps.to(lowerCAmelCase__		)

							__lowercase					     =				[]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = True ,  )	->   Union[SchedulerOutput, Tuple]:

							'''simple docstring'''


							if self.num_inference_steps is None:
											raise ValueError(
											    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

							__lowercase					     =				(self.timesteps == timestep).nonzero().item()
							__lowercase					     =				timestep_index + 1

							__lowercase					     =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(lowerCAmelCase__		)

							if len(self.ets		) == 1:
											__lowercase					     =				self.ets[-1]
							elif len(self.ets		) == 2:
											__lowercase					     =				(3 * self.ets[-1] - self.ets[-2]) / 2
							elif len(self.ets		) == 3:
											__lowercase					     =				(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
							else:
											__lowercase					     =				(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

							__lowercase					     =				self._get_prev_sample(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   torch.FloatTensor:

							'''simple docstring'''


							return sample





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.alphas[timestep_index]
							__lowercase					     =				self.betas[timestep_index]

							__lowercase					     =				self.alphas[prev_timestep_index]
							__lowercase					     =				self.betas[prev_timestep_index]

							__lowercase					     =				(sample - sigma * ets) / max(lowerCAmelCase__ ,  1E-8		)
							__lowercase					     =				next_alpha * pred + ets * next_sigma

							return prev_sample





			def __len__(  self		)	->   Dict:

							'''simple docstring'''


							return self.config.num_train_timesteps
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)